Text Classification
Transformers
Safetensors
Russian
bert
multilabel-classification
requirements-engineering
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use eternalGenius/rubert_level1_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eternalGenius/rubert_level1_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eternalGenius/rubert_level1_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eternalGenius/rubert_level1_v2") model = AutoModelForSequenceClassification.from_pretrained("eternalGenius/rubert_level1_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update thresholds.json
Browse files- thresholds.json +4 -4
thresholds.json
CHANGED
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@@ -2,15 +2,15 @@
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"IsFunctional": 0.4,
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"IsBusiness": 0.45,
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"Other (OT)": 0.4,
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"Availability (A)": 0.
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"Fault Tolerance (FT)": 0.35,
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"Legal (L)": 0.
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"Look & Feel (LF)": 0.75,
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"Maintainability (MN)": 0.3,
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"Operability (O)": 0.6,
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"Performance (PE)": 0.
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"Portability (PO)": 0.25,
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"Scalability (SC)": 0.1,
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"Security (SE)": 0.4,
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-
"Usability (US)": 0.
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}
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"IsFunctional": 0.4,
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"IsBusiness": 0.45,
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"Other (OT)": 0.4,
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+
"Availability (A)": 0.33,
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"Fault Tolerance (FT)": 0.35,
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"Legal (L)": 0.22,
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"Look & Feel (LF)": 0.75,
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"Maintainability (MN)": 0.3,
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"Operability (O)": 0.6,
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"Performance (PE)": 0.35,
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"Portability (PO)": 0.25,
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"Scalability (SC)": 0.1,
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"Security (SE)": 0.4,
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+
"Usability (US)": 0.43
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}
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